Research On Multi-domain Sensor Indoor Positioning Technology Based On Particle Filter

With the development of smart cities,the further development of indoor space,indoor positioning is becoming more and more important,and Global Positioning System(GPS)cannot be effectively applied to it,and sensors are used to complete it.Initially,special positioning equipment is used for indoor positioning.They install some signal transceiving devices indoors,and then the user holds or carries the signal transceiving equipment.However,additional equipment will increase the cost,and it takes time to install the equipment.The popular martphone positioning in recent years has improved this problem,relying on mobile phone sensors to collect data and transmit it to the server via the network,reducing costs and increasing operability.Generally,mobile phone positioning relies on Wi Fi signals to determine the user’s location,and no additional infrastructure is required.However,the positioning accuracy of Wi Fi signals is low and requires complex algorithm calibration.The accuracy of the mobile phone sensor is not high,and the track estimation is not particularly accurate.In order to solve the above problems,this article proposes a multi-domain sensor indoor positioning method based on particle filter.Using multi-domain sensors for data collection,two indoor landmarks are designed: global landmark and local landmark.Observing these landmarks requires the use of different types of mobile phone sensors to improve the robustness to environmental noise interference.A step counting algorithm based on variable step size is designed to estimate the user’s displacement,avoiding the impact of the mobile phone post and the user’s motion state.At the same time,a particle filtering algorithm based on existing Factored Solution to the Simultaneous Localization and Mapping(Fast SLAM)is improved,which has low calculation complexity and high robustness to the correlation problem.The experiment proves that the positioning method proposed in this paper has higherpositioning accuracy and lower delay.